Mobile edge computing (MEC) is a promising solution for enhancing user experience, minimizing content delivery expenses, and reducing backhaul traffic. This paper presents a game-theoretic framework to address the edge resource crowdsourcing problem, where mobile edge devices (MEDs) provide idle storage for content caching in exchange for rewards from a content provider (CP). We model the interaction between the CP and MEDs as a Stackelberg game, with the CP as the leader setting the reward structure and the MEDs as followers competing in a non-cooperative game for these rewards. We propose a novel privacy-preserving method to derive the Stackelberg equilibrium of the game. Notably, our algorithm is designed to operate effectively in time-varying communication networks, addressing the high mobility inherent in MEC environments. This contrasts with state-of-the-art algorithms, which assume a static communication network among MEDs–an impractical condition that does not account for the mobility of MEDs during algorithm execution. Specifically, our approach employs consensus-based algorithms to compute the Nash equilibrium (NE) for MEDs, with MEDs exchanging NE profile estimates with neighbors via row-stochastic mixing matrices and performing gradient steps to optimize their utility in a fully decentralized manner. Based on the computed NE strategies, we propose a zeroth-order reward search algorithm for the CP to determine the optimal strategy for profit maximization. Our comprehensive analysis details the properties of the equilibrium and establishes the geometric convergence of the proposed algorithms to the NE. We also derive explicit bounds for the stepsizes based on the game's properties and the graphs' connectivity structure. Extensive numerical results validate the efficacy of our proposed approach.

This paper studies the joint optimization of edge node activation and resource pricing in edge computing, where an edge computing platform provides heterogeneous resources to accommodate multiple services with diverse pReferences. We cast this problem as a bi-level program, with the platform acting as the leader and the services as the followers. The platform aims to maximize net profit by optimizing edge resource prices and edge node activation, with the services’ optimization problems acting as constraints. Based on the platform’s decisions, each service aims to minimize its costs and enhance user experience through optimal service placement and resource procurement decisions. The presence of integer variables in both the upper and lower-level problems renders this problem particularly challenging. Traditional techniques for transforming bi-level problems into single-level formulations are inappropriate owing to the non-convex nature of the follower problems. Drawing inspiration from the column-and-constraint generation method in robust optimization, we develop an efficient decomposition-based iterative algorithm to compute an exact optimal solution to the formulated bi-level problem. Extensive numerical results are presented to demonstrate the efficacy of the proposed model and technique.

Edge computing enables Service Providers (SPs) to enhance user experience by placing services closer to the network edge. However, cost-effectively provisioning edge resources to meet uncertain and varying demand is a critical challenge. This paper introduces a novel two-stage, multi-period robust optimization model for edge service placement and workload allocation, aiming to minimize SPs' operating costs while ensuring service quality. The salient feature of this model is its ability to leverage dynamic service placement and spatio-temporal correlations in demand uncertainties to mitigate the conservatism of traditional robust approaches optimized for worst-case scenarios. In our model, resource reservation is determined preemptively in the first stage, while dynamic service placement and workload allocation are adaptively optimized in the second stage after uncertainties are revealed. To address the computational challenges posed by integer recourse variables in the resulting tri-level adjustable robust optimization problem, we develop a novel iterative decomposition-based approach with guaranteed finite convergence to an exact optimal solution. Extensive numerical results validate the efficacy of the proposed model and approach.

Edge computing (EC) promises to deliver low-latency and ubiquitous computation to numerous devices at the network edge. This paper aims to jointly optimize edge node (EN) placement and resource allocation for an EC platform, considering demand uncertainty. Diverging from existing approaches treating uncertainties as exogenous, we propose a novel two-stage decision-dependent distributionally robust optimization (DRO) framework to effectively capture the interdependence between EN placement decisions and uncertain demands. The first stage involves making EN placement decisions, while the second stage optimizes resource allocation after uncertainty revelation. We present an exact mixed-integer linear program reformulation for solving the underlying "min-max-min" two-stage model. We further introduce a valid inequality method to enhance computational efficiency, especially for large-scale networks. Extensive numerical experiments demonstrate the benefits of considering endogenous uncertainties and the advantages of the proposed model and approach.

Edge Computing (EC) offers a superior user experience by positioning cloud resources in close proximity to end users. The challenge of allocating edge resources efficiently while maximizing profit for the EC platform remains a sophisticated problem, especially with the added complexity of the online arrival of resource requests. To address this challenge, we propose to cast the problem as a multi-armed bandit problem and develop two novel online pricing mechanisms, the Kullback-Leibler Upper Confidence Bound (KL-UCB) algorithm and the Min-Max Optimal algorithm, for heterogeneous edge resource allocation. These mechanisms operate in real-time and do not require prior knowledge of demand distribution, which can be difficult to obtain in practice. The proposed posted pricing schemes allow users to select and pay for their preferred resources, with the platform dynamically adjusting resource prices based on observed historical data. Numerical results show the advantages of the proposed mechanisms compared to several benchmark schemes derived from traditional bandit algorithms, including the Epsilon-Greedy, basic UCB, and Thompson Sampling algorithms.

Mobile edge computing (MEC) is a promising solution for enhancing the user experience, minimizing content delivery expenses, and reducing backhaul traffic. In this paper, we propose a novel privacy-preserving decentralized game-theoretic framework for resource crowdsourcing in MEC. Our framework models the interactions between a content provider (CP) and multiple mobile edge device users (MEDs) as a non-cooperative game, in which MEDs offer idle storage resources for content caching in exchange for rewards. We introduce efficient decentralized gradient play algorithms for Nash equilibrium (NE) computation by exchanging local information among neighboring MEDs only, thus preventing attackers from learning users' private information. The key challenge in designing such algorithms is that communication among MEDs is not fixed and is facilitated by a sequence of undirected time-varying graphs. Our approach achieves linear convergence to the NE without imposing any assumptions on the values of parameters in the local objective functions, such as requiring strong monotonicity to be stronger than its dependence on other MEDs' actions, which is commonly required in existing literature when the graph is directed time-varying. Extensive simulations demonstrate the effectiveness of our approach in achieving efficient resource outsourcing decisions while preserving the privacy of the edge devices.